Open AccessFeature Extraction from Turbulent Channel Flow Databases via Composite DMD Analysis
Author(s) -
B. Li,
Jesús GaricanoMena,
Eusebio Valero
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1522/1/012008
Subject(s) - dynamic mode decomposition , turbulence , database , reynolds number , composite number , flow (mathematics) , reynolds stress , feature (linguistics) , channel (broadcasting) , computer science , mode (computer interface) , mechanics , physics , algorithm , computer network , linguistics , philosophy , operating system
In this contribution we consider the Dynamic Mode Decomposition (DMD) framework as a purely data-driven tool to investigate a Re τ ≍ 950 turbulent channel database. Specifically, composite-based DMD analyses are conducted, with hybrid snapshots composed by skin friction and Reynolds stress. A small number of dynamic modes (less than 1% of the number of snapshots) is found to be able to recover accurately the DNS Reynolds stresses near the wall, with a weighted factor as an indicator for the modes selections. As a possibility of analysis large turbulent database, we conclude that composite DMD is an attractive, purely data-driven, feature extraction tool to study turbulent flows.